Recent efforts in coding theory have focused on building codes for insertions and deletions, called insdel codes, with optimal trade-offs between their redundancy and their error-correction capabilities, as well as efficient encoding and decoding alg
orithms. In many applications, polynomial running time may still be prohibitively expensive, which has motivated the study of codes with super-efficient decoding algorithms. These have led to the well-studied notions of Locally Decodable Codes (LDCs) and Locally Correctable Codes (LCCs). Inspired by these notions, Ostrovsky and Paskin-Cherniavsky (Information Theoretic Security, 2015) generalized Hamming LDCs to insertions and deletions. To the best of our knowledge, these are the only known results that study the analogues of Hamming LDCs in channels performing insertions and deletions. Here we continue the study of insdel codes that admit local algorithms. Specifically, we reprove the results of Ostrovsky and Paskin-Cherniavsky for insdel LDCs using a different set of techniques. We also observe that the techniques extend to constructions of LCCs. Specifically, we obtain insdel LDCs and LCCs from their Hamming LDCs and LCCs analogues, respectively. The rate and error-correction capability blow up only by a constant factor, while the query complexity blows up by a poly log factor in the block length. Since insdel locally decodable/correctble codes are scarcely studied in the literature, we believe our results and techniques may lead to further research. In particular, we conjecture that constant-query insdel LDCs/LCCs do not exist.
Conducting materials typically exhibit either diffusive or ballistic charge transport. However, when electron-electron interactions dominate, a hydrodynamic regime with viscous charge flow emerges (1-13). More stringent conditions eventually yield a
quantum-critical Dirac-fluid regime, where electronic heat can flow more efficiently than charge (14-22). Here we observe heat transport in graphene in the diffusive and hydrodynamic regimes, and report a controllable transition to the Dirac-fluid regime at room temperature, using carrier temperature and carrier density as control knobs. We introduce the technique of spatiotemporal thermoelectric microscopy with femtosecond temporal and nanometre spatial resolution, which allows for tracking electronic heat spreading. In the diffusive regime, we find a thermal diffusivity of $sim$2,000 cm$^2$/s, consistent with charge transport. Remarkably, during the hydrodynamic time window before momentum relaxation, we observe heat spreading corresponding to a giant diffusivity up to 70,000 cm$^2$/Vs, indicative of a Dirac fluid. These results are promising for applications such as nanoscale thermal management.
The ultrafast response of metals to light is governed by intriguing non-equilibrium dynamics involving the interplay of excited electrons and phonons. The coupling between them gives rise to nonlinear diffusion behavior on ultrashort timescales. Here
, we use scanning ultrafast thermo-modulation microscopy to image the spatio-temporal hot-electron diffusion in a thin gold film. By tracking local transient reflectivity with 20 nm and 0.25 ps resolution, we reveal two distinct diffusion regimes, consisting of an initial rapid diffusion during the first few picoseconds after optical excitation, followed by about 100-fold slower diffusion at longer times. We simulate the thermo-optical response of the gold film with a comprehensive three-dimensional model, and identify the two regimes as hot-electron and phonon-limited thermal diffusion, respectively.
The detection and analysis of events within massive collections of time-series has become an extremely important task for time-domain astronomy. In particular, many scientific investigations (e.g. the analysis of microlensing and other transients) be
gin with the detection of isolated events in irregularly-sampled series with both non-linear trends and non-Gaussian noise. We outline a semi-parametric, robust, parallel method for identifying variability and isolated events at multiple scales in the presence of the above complications. This approach harnesses the power of Bayesian modeling while maintaining much of the speed and scalability of more ad-hoc machine learning approaches. We also contrast this work with event detection methods from other fields, highlighting the unique challenges posed by astronomical surveys. Finally, we present results from the application of this method to 87.2 million EROS-2 sources, where we have obtained a greater than 100-fold reduction in candidates for certain types of phenomena while creating high-quality features for subsequent analyses.
